Noble-gas-infused neoprene closed-cell foams achieving ultra-low thermal conductivity fabrics

Jeffrey L. Moran, Anton L. Cottrill, Jesse D. Benck, Pingwei Liu, Zhe Yuan, Michael S. Strano, Jacopo Buongiorno

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Closed-cell foams are widely applied as insulation and essential for the thermal management of protective garments for extreme environments. In this work, we develop and demonstrate a strategy for drastically reducing the thermal conductivity of a flexible, closed-cell polychloroprene foam to 0.031 ± 0.002 W m−1 K−1, approaching values of an air gap (0.027 W m−1 K−1) for an extended period of time (>10 hours), within a material capable of textile processing. Ultra-insulating neoprene materials are synthesized using high-pressure processing at 243 kPa in a high-molecular-weight gas environment, such as Ar, Kr, or Xe. A Fickian diffusion model describes both the mass infusion and thermal conductivity reduction of the foam as a function of processing time, predicting a 24–72 hour required exposure time for full charging of a 6 mm thick 5 cm diameter neoprene sample. These results enable waterproof textile insulation that approximates a wearable air gap. We demonstrate a wetsuit made of ultra-low thermally conductive neoprene capable of potentially extending dive times to 2–3 hours in water below 10 °C, compared with
Original languageEnglish (US)
Pages (from-to)21389-21398
Number of pages10
JournalRSC Advances
Issue number38
StatePublished - 2018
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: The authors thank Dr Matteo Bucci, Kristen Young, and Albert Tianxiang Liu for helpful discussions. The authors acknowledge the Office of Naval Research (ONR), under award N00014-16-1-2144, for their support in developing and analyzing the low thermal conductivity materials for application in low temperature divesuits, and we acknowledge King Abdullah University of Science and Technology (KAUST), under award OSR-2015-Sensors-2700, for their financial support regarding this project. We also acknowledge The US Department of Energy, Office of Science, Basic Energy Sciences under award grant DE-FG02-08ER46488 Mod 0008 is acknowledged for support of mathematical modeling and computation relating to energy systems related to nanomaterials.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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